Digital health and data solutions are essential to helping federal agencies address social determinants of health (SDOH) and achieve other national policy objectives.
Imagine the impact of a health system armed with the data to identify patients found to have the social risk factors associated with a chronic condition–enabling an asthma sufferer, for instance, to easily navigate to resources or incentives offering options for improved air quality and safe outlets for physical activity. What about a public health surveillance system that integrates genomics and social media mining into its big data analyses? Or one that produces targeted programming to address questions about the health impact of an industrial facility with air discharge?
Federal agencies don’t need to wait to convert this thought leadership into turnkey action. Each of these examples may be realized today for federal agencies thinking about optimizing their public health situational awareness.
Using digital health interventions to achieve collective impact in addressing the SDOH
Federal and community data are increasingly available via OpenAPIs and may be used to inform public health programming or digital health data architectures that are relevant to addressing the SDOH. These machine and human-readable documents are exposed in a standard format and structure, and they may be used together to develop new composite insight. However, not every OpenAPI operates the same way. Bridging data across sectors to produce harmonized insight begs for excellence in Enterprise IT services delivery and subject matter expertise on the underlying environments the OpenAPIs function within. For instance, it’s foreseeable that organizations in the health, education, and public safety sectors would need to be bridged when examining the health impact of an industrial discharge and designing a digital health-enabled intervention.
The following examples illustrate the above approach to developing digital health interventions that address the SDOH across sectors using the hypothetical industrial discharge case.
SMART on FHIR applications that address SDOH using OpenAPIs and microservices
Design and development of digital health applications that draw from a variety of data contributors may be supported by the modular, scalable, autonomous facets of an architectural approach to data integration, such as one that utilizes microservices and service meshes to integrate OpenAPIs. Enabling data exchange and integration using microservices and service meshes calls for trusted partnering to meet data stewards at the stakeholder’s place of digital maturity and in the language of their specialty of practice. Taken together, this approach to the solution development lifecycle improves its adoption and sustainability, while bridging disparate sectors to achieve equitable digital health interventions and collective impact.
Digital health intervention design must draw from expertise on functional environments of air quality measurement, urban planning stakeholders, and the health system to define the parameters of the social risk scores, while simultaneously building the technical tools to affect behavioral change at the point of care. A SMART on FHIR application that runs natively in the Electronic Health Record (EHR) system and draws from multiple hypothetical OpenAPIs, including the EHR itself, the EPA’s Air Quality System (AQS), the American Community Survey, the National Park Service, and other community-based datastores, may be harmonized in a microservices and service mesh architecture.
Epigenetics and Pathogen Genomics
The use of genetics is critical to investigating outbreaks and developing vaccines. Much work in and around genomics occurs in research universities with large data storage and computing capacities for analysis and research. Bringing together a public-private partnership that allows analyses to be conducted across entities would benefit from enterprise IT cloud services that provide a capacity for linking together disparate systems and distributing computing and storage resources. For example, it may be of value to integrate information from the National Cancer Institute’s Genomic Data Commons with a proprietary gold-certified cancer registry and local air quality data to surveil the effects of nearby industrial air discharge on incidences of cancer in the community. Even further, investigators may analyze a given pathogen that interacts with this subpopulation to determine if their having cancer impacts the genetic makeup of the pathogen.
Social Media Mining
Methods for the automatic detection and extraction of health-related concepts in social media enable inferences to be made from associated geographic, demographic, public health, and clinical data. The automatic analysis of social media messages has use cases for disease surveillance, behavioral health, programmatic evaluation, and adverse drug events. Additionally, work on forecasting future levels of disease prevalence weeks into the future may be achieved through improved sentiment analyses. Keeping with the example of industrial air discharge, the policy objective of reducing symptoms of asthma may be achieved through brokered partnerships with industry leaders in social media to scrape information about asthmatic symptoms (e.g., word identification, sentiment analysis, etc.) in a particular geography.
Artificial Intelligence/Machine Learning (AI/ML)
AI/ML may be used to integrate social risk factors with sample clinical data to gain better insight for predicting problems and conditions associated with our example of industrial air discharge having an adverse effect on a community. Knowing that it is much more valuable for the end user to know the reasons behind the risk, and the community level social risk factors, a SMART on FHIR application may be created to explain the underpinnings of an AI-generated prediction that care coordinators should use to link a particular patient to resources that may reduce their risk of asthma attacks.
Addressing SDOH is one of many national public health policy objectives, yet it is an arena that holds immediate promise due to technologies like those outlined above. The successful creation of this digital health mosaic will be realized most efficiently through the strategic collaboration of federal agencies and their trusted partners who can deliver subject matter expertise across sectors and enterprise IT services for optimized delivery of turnkey solutions.
Achieving collective impact requires integrating technical solutions that interact with different sectors of society, as well as an implementation partner with a robust understanding of those functional environments and their constraints. Peraton provides public health informatics and information technology services to leading public health agencies, supporting every major public health event over the past 20 years from SARS to COVID-19. Peraton advances public health outcomes by providing the global health community rapid access to technology and science to strengthen practice, promote health, prevent disease, and prepare for and respond to adverse health events.
Rahul Jain is a Health Information Exchange Architect with experience in harmonized EHR, quantified self-data, and digital health product and program design working in Peraton’s Defense Mission and Health Solutions Sector.